Festival of Data Technology

In this training taster, dive into two frameworks for becoming successful with data: Value Chain of Data Science: A battle-tested framework to think about analytics solutions end-to-end. Analytics Maturity Journey: A way to assess where an organization stands today, and in what leadership should invest, to become an effective data-driven organizations.

Data is constantly growing, but screen are getting smaller. A bar chart with an eternal scrolling bar won't cut it. On this presentation, I will show some different graph types and tricks that look stunning (regardless of the granularity of your data).

A popular topic is stories from analists becoming analytics engineers, but I actually came from the other side of the spectrum. Some of my views on this path.

Data is everywhere. It is through analysis and visualization that we are able to turn data into information that can be used to drive better decision making. Out-of-the-box tools will allow you to create a chart, but if you want people to take action, your numbers need to tell a compelling story. Learn how elements of storytelling can be applied to data visualization.

MLFlow has been widely used as a reliable ML experiment tracking and model registry to pave the way towards MLOps. However, its flexibility also imposes challenges on having a robust and consistent way working across different scenarios. In this talk we will have a closer look at how to properly use MLFlow in combination with other open source frameworks such as Airflow and Kubernetes to further mature the lifecycle of machine learning use cases.

With dbt, an open-source piece of magic, you can transform the data you access in your warehouses simply with the power of SQL select statements. This will give data users - such as data analysts, data scientists, and business users - more autonomy, empowerment and understanding when it comes to gathering valuable insights from the data. Learn how to use materializations to maximize your warehouse usage, automated testing as coverage to give you confidence in your data, and dbt docs to generate documentation to share key information like lineage and key business logic.

From inventory to website visitors, resource planning to finance and accounting, much of any organization’s data has temporal nature. Key problems facing business include not only forecasting the future values, but also identifying when these values are alarming. Intertemporal anomaly detection often becomes a complex problem that neither common rule-based heuristics nor standard anomaly detection algorithms can tackle well. In this webinar we will have a close look at this domain, learn about its distinctive features and challenges as well as learn to detect anomalies in time series data, while dodging many common mistakes and issues.

With the democratization of data via data lakes data science teams increasingly rely on custom model pipelines for data preprocessing and feature engineering. As a result it becomes difficult to reuse features or even compare similar features across different teams. This can be a significant challenge, as it can lead to duplicative work and ambiguous definitions that cause confusion and a risk on wrong conclusions. For example, two data science teams that both communicate an average click-rate to the marketing team, where one team excludes clicks made by robots and the other doesn’t. Without being aware of this different interpretation the marketing team can make some awfully wrong decisions.